ConvGeNBenchmarkResults/data_result/convGAN-majority-5/imblearn_protein_homo.log

///////////////////////////////////////////
// Running convGAN-majority-5 on imblearn_protein_homo
///////////////////////////////////////////

Load 'data_input/imblearn_protein_homo'
from imblearn
Data loaded.
-> Shuffling data
### Start exercise for synthetic point generator

====== Step 1/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 1/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with GAN.predict
GAN tn, fp: 28661, 230
GAN fn, tp: 40, 220
GAN f1 score: 0.620
GAN cohens kappa score: 0.615

-> test with 'LR'
LR tn, fp: 27665, 1226
LR fn, tp: 16, 244
LR f1 score: 0.282
LR cohens kappa score: 0.271
LR average precision score: 0.856

-> test with 'GB'
GB tn, fp: 28409, 482
GB fn, tp: 19, 241
GB f1 score: 0.490
GB cohens kappa score: 0.484

-> test with 'KNN'
KNN tn, fp: 28546, 345
KNN fn, tp: 96, 164
KNN f1 score: 0.427
KNN cohens kappa score: 0.420


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with GAN.predict
GAN tn, fp: 28614, 277
GAN fn, tp: 32, 228
GAN f1 score: 0.596
GAN cohens kappa score: 0.591

-> test with 'LR'
LR tn, fp: 27811, 1080
LR fn, tp: 15, 245
LR f1 score: 0.309
LR cohens kappa score: 0.299
LR average precision score: 0.886

-> test with 'GB'
GB tn, fp: 28409, 482
GB fn, tp: 15, 245
GB f1 score: 0.496
GB cohens kappa score: 0.490

-> test with 'KNN'
KNN tn, fp: 28324, 567
KNN fn, tp: 80, 180
KNN f1 score: 0.357
KNN cohens kappa score: 0.349


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with GAN.predict
GAN tn, fp: 28694, 197
GAN fn, tp: 42, 218
GAN f1 score: 0.646
GAN cohens kappa score: 0.642

-> test with 'LR'
LR tn, fp: 27637, 1254
LR fn, tp: 7, 253
LR f1 score: 0.286
LR cohens kappa score: 0.275
LR average precision score: 0.886

-> test with 'GB'
GB tn, fp: 28369, 522
GB fn, tp: 10, 250
GB f1 score: 0.484
GB cohens kappa score: 0.478

-> test with 'KNN'
KNN tn, fp: 28486, 405
KNN fn, tp: 110, 150
KNN f1 score: 0.368
KNN cohens kappa score: 0.360


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with GAN.predict
GAN tn, fp: 28678, 213
GAN fn, tp: 45, 215
GAN f1 score: 0.625
GAN cohens kappa score: 0.621

-> test with 'LR'
LR tn, fp: 27749, 1142
LR fn, tp: 14, 246
LR f1 score: 0.299
LR cohens kappa score: 0.288
LR average precision score: 0.857

-> test with 'GB'
GB tn, fp: 28437, 454
GB fn, tp: 19, 241
GB f1 score: 0.505
GB cohens kappa score: 0.498

-> test with 'KNN'
KNN tn, fp: 28499, 392
KNN fn, tp: 94, 166
KNN f1 score: 0.406
KNN cohens kappa score: 0.399


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114524 synthetic samples
-> test with GAN.predict
GAN tn, fp: 28596, 295
GAN fn, tp: 49, 207
GAN f1 score: 0.546
GAN cohens kappa score: 0.541

-> test with 'LR'
LR tn, fp: 27808, 1083
LR fn, tp: 20, 236
LR f1 score: 0.300
LR cohens kappa score: 0.289
LR average precision score: 0.818

-> test with 'GB'
GB tn, fp: 28506, 385
GB fn, tp: 27, 229
GB f1 score: 0.526
GB cohens kappa score: 0.520

-> test with 'KNN'
KNN tn, fp: 28504, 387
KNN fn, tp: 113, 143
KNN f1 score: 0.364
KNN cohens kappa score: 0.356


====== Step 2/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with GAN.predict
GAN tn, fp: 28700, 191
GAN fn, tp: 47, 213
GAN f1 score: 0.642
GAN cohens kappa score: 0.638

-> test with 'LR'
LR tn, fp: 27768, 1123
LR fn, tp: 11, 249
LR f1 score: 0.305
LR cohens kappa score: 0.295
LR average precision score: 0.866

-> test with 'GB'
GB tn, fp: 28459, 432
GB fn, tp: 18, 242
GB f1 score: 0.518
GB cohens kappa score: 0.512

-> test with 'KNN'
KNN tn, fp: 28547, 344
KNN fn, tp: 100, 160
KNN f1 score: 0.419
KNN cohens kappa score: 0.412


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with GAN.predict
GAN tn, fp: 28593, 298
GAN fn, tp: 33, 227
GAN f1 score: 0.578
GAN cohens kappa score: 0.573

-> test with 'LR'
LR tn, fp: 27734, 1157
LR fn, tp: 13, 247
LR f1 score: 0.297
LR cohens kappa score: 0.286
LR average precision score: 0.891

-> test with 'GB'
GB tn, fp: 28376, 515
GB fn, tp: 18, 242
GB f1 score: 0.476
GB cohens kappa score: 0.469

-> test with 'KNN'
KNN tn, fp: 28525, 366
KNN fn, tp: 93, 167
KNN f1 score: 0.421
KNN cohens kappa score: 0.414


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with GAN.predict
GAN tn, fp: 28408, 483
GAN fn, tp: 47, 213
GAN f1 score: 0.446
GAN cohens kappa score: 0.438

-> test with 'LR'
LR tn, fp: 27736, 1155
LR fn, tp: 17, 243
LR f1 score: 0.293
LR cohens kappa score: 0.282
LR average precision score: 0.833

-> test with 'GB'
GB tn, fp: 28429, 462
GB fn, tp: 22, 238
GB f1 score: 0.496
GB cohens kappa score: 0.489

-> test with 'KNN'
KNN tn, fp: 28497, 394
KNN fn, tp: 99, 161
KNN f1 score: 0.395
KNN cohens kappa score: 0.388


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with GAN.predict
GAN tn, fp: 28715, 176
GAN fn, tp: 41, 219
GAN f1 score: 0.669
GAN cohens kappa score: 0.665

-> test with 'LR'
LR tn, fp: 27708, 1183
LR fn, tp: 14, 246
LR f1 score: 0.291
LR cohens kappa score: 0.280
LR average precision score: 0.863

-> test with 'GB'
GB tn, fp: 28422, 469
GB fn, tp: 16, 244
GB f1 score: 0.502
GB cohens kappa score: 0.495

-> test with 'KNN'
KNN tn, fp: 28502, 389
KNN fn, tp: 90, 170
KNN f1 score: 0.415
KNN cohens kappa score: 0.408


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114524 synthetic samples
-> test with GAN.predict
GAN tn, fp: 28634, 257
GAN fn, tp: 46, 210
GAN f1 score: 0.581
GAN cohens kappa score: 0.576

-> test with 'LR'
LR tn, fp: 27656, 1235
LR fn, tp: 13, 243
LR f1 score: 0.280
LR cohens kappa score: 0.269
LR average precision score: 0.845

-> test with 'GB'
GB tn, fp: 28396, 495
GB fn, tp: 19, 237
GB f1 score: 0.480
GB cohens kappa score: 0.473

-> test with 'KNN'
KNN tn, fp: 28518, 373
KNN fn, tp: 97, 159
KNN f1 score: 0.404
KNN cohens kappa score: 0.396


====== Step 3/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with GAN.predict
GAN tn, fp: 28708, 183
GAN fn, tp: 42, 218
GAN f1 score: 0.660
GAN cohens kappa score: 0.656

-> test with 'LR'
LR tn, fp: 27787, 1104
LR fn, tp: 17, 243
LR f1 score: 0.302
LR cohens kappa score: 0.292
LR average precision score: 0.868

-> test with 'GB'
GB tn, fp: 28456, 435
GB fn, tp: 20, 240
GB f1 score: 0.513
GB cohens kappa score: 0.507

-> test with 'KNN'
KNN tn, fp: 28501, 390
KNN fn, tp: 91, 169
KNN f1 score: 0.413
KNN cohens kappa score: 0.405


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with GAN.predict
GAN tn, fp: 28752, 139
GAN fn, tp: 41, 219
GAN f1 score: 0.709
GAN cohens kappa score: 0.706

-> test with 'LR'
LR tn, fp: 27705, 1186
LR fn, tp: 12, 248
LR f1 score: 0.293
LR cohens kappa score: 0.282
LR average precision score: 0.863

-> test with 'GB'
GB tn, fp: 28407, 484
GB fn, tp: 18, 242
GB f1 score: 0.491
GB cohens kappa score: 0.484

-> test with 'KNN'
KNN tn, fp: 28539, 352
KNN fn, tp: 108, 152
KNN f1 score: 0.398
KNN cohens kappa score: 0.391


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with GAN.predict
GAN tn, fp: 27444, 1447
GAN fn, tp: 34, 226
GAN f1 score: 0.234
GAN cohens kappa score: 0.222

-> test with 'LR'
LR tn, fp: 27732, 1159
LR fn, tp: 17, 243
LR f1 score: 0.292
LR cohens kappa score: 0.282
LR average precision score: 0.830

-> test with 'GB'
GB tn, fp: 28434, 457
GB fn, tp: 22, 238
GB f1 score: 0.498
GB cohens kappa score: 0.492

-> test with 'KNN'
KNN tn, fp: 28523, 368
KNN fn, tp: 101, 159
KNN f1 score: 0.404
KNN cohens kappa score: 0.397


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with GAN.predict
GAN tn, fp: 28674, 217
GAN fn, tp: 40, 220
GAN f1 score: 0.631
GAN cohens kappa score: 0.627

-> test with 'LR'
LR tn, fp: 27674, 1217
LR fn, tp: 12, 248
LR f1 score: 0.288
LR cohens kappa score: 0.277
LR average precision score: 0.865

-> test with 'GB'
GB tn, fp: 28435, 456
GB fn, tp: 10, 250
GB f1 score: 0.518
GB cohens kappa score: 0.511

-> test with 'KNN'
KNN tn, fp: 28495, 396
KNN fn, tp: 99, 161
KNN f1 score: 0.394
KNN cohens kappa score: 0.387


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114524 synthetic samples
-> test with GAN.predict
GAN tn, fp: 28663, 228
GAN fn, tp: 36, 220
GAN f1 score: 0.625
GAN cohens kappa score: 0.621

-> test with 'LR'
LR tn, fp: 27697, 1194
LR fn, tp: 12, 244
LR f1 score: 0.288
LR cohens kappa score: 0.277
LR average precision score: 0.882

-> test with 'GB'
GB tn, fp: 28412, 479
GB fn, tp: 16, 240
GB f1 score: 0.492
GB cohens kappa score: 0.486

-> test with 'KNN'
KNN tn, fp: 28504, 387
KNN fn, tp: 92, 164
KNN f1 score: 0.406
KNN cohens kappa score: 0.399


====== Step 4/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with GAN.predict
GAN tn, fp: 28749, 142
GAN fn, tp: 39, 221
GAN f1 score: 0.709
GAN cohens kappa score: 0.706

-> test with 'LR'
LR tn, fp: 27806, 1085
LR fn, tp: 13, 247
LR f1 score: 0.310
LR cohens kappa score: 0.300
LR average precision score: 0.873

-> test with 'GB'
GB tn, fp: 28419, 472
GB fn, tp: 16, 244
GB f1 score: 0.500
GB cohens kappa score: 0.493

-> test with 'KNN'
KNN tn, fp: 28105, 786
KNN fn, tp: 90, 170
KNN f1 score: 0.280
KNN cohens kappa score: 0.269


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with GAN.predict
GAN tn, fp: 28647, 244
GAN fn, tp: 45, 215
GAN f1 score: 0.598
GAN cohens kappa score: 0.593

-> test with 'LR'
LR tn, fp: 27761, 1130
LR fn, tp: 15, 245
LR f1 score: 0.300
LR cohens kappa score: 0.289
LR average precision score: 0.839

-> test with 'GB'
GB tn, fp: 28379, 512
GB fn, tp: 20, 240
GB f1 score: 0.474
GB cohens kappa score: 0.467

-> test with 'KNN'
KNN tn, fp: 28527, 364
KNN fn, tp: 105, 155
KNN f1 score: 0.398
KNN cohens kappa score: 0.391


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with GAN.predict
GAN tn, fp: 28737, 154
GAN fn, tp: 45, 215
GAN f1 score: 0.684
GAN cohens kappa score: 0.680

-> test with 'LR'
LR tn, fp: 27737, 1154
LR fn, tp: 18, 242
LR f1 score: 0.292
LR cohens kappa score: 0.281
LR average precision score: 0.855

-> test with 'GB'
GB tn, fp: 28447, 444
GB fn, tp: 20, 240
GB f1 score: 0.508
GB cohens kappa score: 0.502

-> test with 'KNN'
KNN tn, fp: 28499, 392
KNN fn, tp: 89, 171
KNN f1 score: 0.416
KNN cohens kappa score: 0.408


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with GAN.predict
GAN tn, fp: 28675, 216
GAN fn, tp: 45, 215
GAN f1 score: 0.622
GAN cohens kappa score: 0.618

-> test with 'LR'
LR tn, fp: 27710, 1181
LR fn, tp: 11, 249
LR f1 score: 0.295
LR cohens kappa score: 0.284
LR average precision score: 0.879

-> test with 'GB'
GB tn, fp: 28466, 425
GB fn, tp: 14, 246
GB f1 score: 0.528
GB cohens kappa score: 0.522

-> test with 'KNN'
KNN tn, fp: 28523, 368
KNN fn, tp: 93, 167
KNN f1 score: 0.420
KNN cohens kappa score: 0.413


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114524 synthetic samples
-> test with GAN.predict
GAN tn, fp: 28660, 231
GAN fn, tp: 31, 225
GAN f1 score: 0.632
GAN cohens kappa score: 0.628

-> test with 'LR'
LR tn, fp: 27747, 1144
LR fn, tp: 16, 240
LR f1 score: 0.293
LR cohens kappa score: 0.282
LR average precision score: 0.839

-> test with 'GB'
GB tn, fp: 28389, 502
GB fn, tp: 15, 241
GB f1 score: 0.482
GB cohens kappa score: 0.476

-> test with 'KNN'
KNN tn, fp: 28492, 399
KNN fn, tp: 89, 167
KNN f1 score: 0.406
KNN cohens kappa score: 0.399


====== Step 5/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with GAN.predict
GAN tn, fp: 28616, 275
GAN fn, tp: 36, 224
GAN f1 score: 0.590
GAN cohens kappa score: 0.585

-> test with 'LR'
LR tn, fp: 27773, 1118
LR fn, tp: 13, 247
LR f1 score: 0.304
LR cohens kappa score: 0.293
LR average precision score: 0.863

-> test with 'GB'
GB tn, fp: 28432, 459
GB fn, tp: 17, 243
GB f1 score: 0.505
GB cohens kappa score: 0.499

-> test with 'KNN'
KNN tn, fp: 28548, 343
KNN fn, tp: 100, 160
KNN f1 score: 0.419
KNN cohens kappa score: 0.412


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with GAN.predict
GAN tn, fp: 28606, 285
GAN fn, tp: 40, 220
GAN f1 score: 0.575
GAN cohens kappa score: 0.570

-> test with 'LR'
LR tn, fp: 27745, 1146
LR fn, tp: 14, 246
LR f1 score: 0.298
LR cohens kappa score: 0.287
LR average precision score: 0.868

-> test with 'GB'
GB tn, fp: 28444, 447
GB fn, tp: 18, 242
GB f1 score: 0.510
GB cohens kappa score: 0.504

-> test with 'KNN'
KNN tn, fp: 28518, 373
KNN fn, tp: 100, 160
KNN f1 score: 0.404
KNN cohens kappa score: 0.396


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with GAN.predict
GAN tn, fp: 28741, 150
GAN fn, tp: 46, 214
GAN f1 score: 0.686
GAN cohens kappa score: 0.683

-> test with 'LR'
LR tn, fp: 27678, 1213
LR fn, tp: 18, 242
LR f1 score: 0.282
LR cohens kappa score: 0.271
LR average precision score: 0.853

-> test with 'GB'
GB tn, fp: 28471, 420
GB fn, tp: 17, 243
GB f1 score: 0.527
GB cohens kappa score: 0.520

-> test with 'KNN'
KNN tn, fp: 28515, 376
KNN fn, tp: 106, 154
KNN f1 score: 0.390
KNN cohens kappa score: 0.382


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114528 synthetic samples
-> test with GAN.predict
GAN tn, fp: 28765, 126
GAN fn, tp: 44, 216
GAN f1 score: 0.718
GAN cohens kappa score: 0.715

-> test with 'LR'
LR tn, fp: 27728, 1163
LR fn, tp: 10, 250
LR f1 score: 0.299
LR cohens kappa score: 0.288
LR average precision score: 0.863

-> test with 'GB'
GB tn, fp: 28408, 483
GB fn, tp: 18, 242
GB f1 score: 0.491
GB cohens kappa score: 0.485

-> test with 'KNN'
KNN tn, fp: 28522, 369
KNN fn, tp: 95, 165
KNN f1 score: 0.416
KNN cohens kappa score: 0.409


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 114524 synthetic samples
-> test with GAN.predict
GAN tn, fp: 28463, 428
GAN fn, tp: 33, 223
GAN f1 score: 0.492
GAN cohens kappa score: 0.485

-> test with 'LR'
LR tn, fp: 27734, 1157
LR fn, tp: 15, 241
LR f1 score: 0.291
LR cohens kappa score: 0.281
LR average precision score: 0.849

-> test with 'GB'
GB tn, fp: 28391, 500
GB fn, tp: 15, 241
GB f1 score: 0.483
GB cohens kappa score: 0.477

-> test with 'KNN'
KNN tn, fp: 28519, 372
KNN fn, tp: 91, 165
KNN f1 score: 0.416
KNN cohens kappa score: 0.409

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 27811, 1254
LR fn, tp: 20, 253
LR f1 score: 0.310
LR cohens kappa score: 0.300
LR average precision score: 0.891


average:
LR tn, fp: 27731.44, 1159.56
LR fn, tp: 14.12, 245.08
LR f1 score: 0.295
LR cohens kappa score: 0.284
LR average precision score: 0.860


minimum:
LR tn, fp: 27637, 1080
LR fn, tp: 7, 236
LR f1 score: 0.280
LR cohens kappa score: 0.269
LR average precision score: 0.818


-----[ GB ]-----
maximum:
GB tn, fp: 28506, 522
GB fn, tp: 27, 250
GB f1 score: 0.528
GB cohens kappa score: 0.522


average:
GB tn, fp: 28424.08, 466.92
GB fn, tp: 17.56, 241.64
GB f1 score: 0.500
GB cohens kappa score: 0.493


minimum:
GB tn, fp: 28369, 385
GB fn, tp: 10, 229
GB f1 score: 0.474
GB cohens kappa score: 0.467


-----[ KNN ]-----
maximum:
KNN tn, fp: 28548, 786
KNN fn, tp: 113, 180
KNN f1 score: 0.427
KNN cohens kappa score: 0.420


average:
KNN tn, fp: 28491.12, 399.88
KNN fn, tp: 96.84, 162.36
KNN f1 score: 0.398
KNN cohens kappa score: 0.391


minimum:
KNN tn, fp: 28105, 343
KNN fn, tp: 80, 143
KNN f1 score: 0.280
KNN cohens kappa score: 0.269


-----[ GAN ]-----
maximum:
GAN tn, fp: 28765, 1447
GAN fn, tp: 49, 228
GAN f1 score: 0.718
GAN cohens kappa score: 0.715


average:
GAN tn, fp: 28607.72, 283.28
GAN fn, tp: 40.76, 218.44
GAN f1 score: 0.605
GAN cohens kappa score: 0.600


minimum:
GAN tn, fp: 27444, 126
GAN fn, tp: 31, 207
GAN f1 score: 0.234
GAN cohens kappa score: 0.222